ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation
This work addresses the high cost of building large, annotated datasets for computational pathology by enabling stain domain translation, though it appears incremental as it builds on existing translation methods for a specific domain.
The paper tackled the problem of generating in-silico immunohistochemistry (IHC) images by translating stains from immunofluorescence (IF) images, resulting in improved nucleus segmentation models that outperformed baseline methods qualitatively and quantitatively.
The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the potential to significantly lower the cost associated with building large and pixel precise datasets needed to train supervised deep learning models. We propose a novel approach for the generation of in-silico immunohistochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images. The proposed approach qualitatively and quantitatively outperforms baseline methods as proven by training nucleus segmentation models on the created in-silico datasets.